On hourly home peak load prediction

• Published in: 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm) pp. 163 - 168
• Main Authors: Singh, R. P., Gao, P. X., Lizotte, D. J.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2012
• Subjects: Accuracy; Artificial neural networks; Correlation; Energy consumption; Load modeling; Predictive models; Time series analysis
• ISBN: 9781467309103; 1467309109
• DOI: 10.1109/SmartGridComm.2012.6485977

The Ontario electrical grid is sized to meet peak electricity load. A reduction in peak load would allow deferring large infrastructural costs of additional power plants, thereby lowering generation cost and electricity prices. Proposed solutions for peak load reduction include demand response and storage. Both these solutions require accurate prediction of a home's peak and mean load. Existing work has focused only on mean load prediction. We find that these methods exhibit high error when predicting peak load. Moreover, a home's historic peak load and occupancy is a better predictor of peak load than observable physical characteristics such as temperature and season. We explore the use of Seasonal Auto Regressive Moving Average (SARMA) for peak load prediction and find that it has 30% lower root mean square error than best known prior methods.